Data Analytics in Test: Recognizing and Reducing Subjectivity

Applying data analytics in production test has become a widely adopted industrial practice in recent years. As the complexity of semiconductor devices scales and the amounts of available test data continue to grow, the research direction in this field is forced to shift away from solving specific problems with ad hoc approaches and demands for deeper understanding of the fundamental issues. Two data-driven test applications where this shift is apparent are production yield optimization and defect screening, where the respective underlying data analytics approaches are correlation analysis and outlier analysis. A core issue present in these two approaches stems from the subjectivity that is inherent to data analytics. This dissertation delves into how subjectivity manifests itself and what can be done to reduce it with respect to the two test applications.Outlier analysis is an approach used for identifying anomalies. The main goal of outlier analysis in test is to capture statistically outlying parts with the hope that their abnormal behavior is attributed to some defectivity. During creation of an outlier model, the decisions about outlying behavior in the existing data are made by utilizing known failures and the test engineer's best judgment. In practice, outlier screening methods are simply used for transforming data into an outlier score space. Even if outlier analysis techniques are able to successfully classify a dataset into inliers and outliers, outlier models require thresholds to be decided. A concept called Consistency is introduced to provide an objective data-driven way to evaluate outlier models by utilizing all available data. The key observation underlying this concept is that outlier analysis should be immune to noise introduced by sources of systematic variation.Correlation analysis is a process comprising a search for related variables. The application of production yield optimization involves searching for correlation between the yield and various controllable parameters. The goal of this process is to uncover parameters that, when adjusted, can result in yield improvement. This analytics process is subjective to the perspective of the analyst and the quality of the result is highly dependent on the analyst’s previous experiences. In order to reduce the subjectivity in this application, a process mining methodology is introduced to learn from the experiences of analysts. The key advantage of this methodology is that in addition to having the capability to record and reproduce these analyses, it can also generalize to analytics processes not contained in the learned experiences

Mitochondrial Physiology and Gene Expression Analyses Reveal Metabolic and Translational Dysregulation in Oocyte-Induced Somatic Nuclear Reprogramming

While reprogramming a foreign nucleus after somatic cell nuclear transfer (SCNT), the enucleated oocyte (ooplasm) must signal that biomass and cellular requirements changed compared to the nucleus donor cell. Using cells expressing nuclear-encoded but mitochondria-targeted EGFP, a strategy was developed to directly distinguish maternal and embryonic products, testing ooplasm demands on transcriptional and post-transcriptional activity during reprogramming. Specifically, we compared transcript and protein levels for EGFP and other products in pre-implantation SCNT embryos, side-by-side to fertilized controls (embryos produced from the same oocyte pool, by intracytoplasmic injection of sperm containing the EGFP transgene). We observed that while EGFP transcript abundance is not different, protein levels are significantly lower in SCNT compared to fertilized blastocysts. This was not observed for Gapdh and Actb, whose protein reflected mRNA. This transcript-protein relationship indicates that the somatic nucleus can keep up with ooplasm transcript demands, whilst transcription and translation mismatch occurs after SCNT for certain mRNAs. We further detected metabolic disturbances after SCNT, suggesting a place among forces regulating post-transcriptional changes during reprogramming. Our observations ascribe oocyte-induced reprogramming with previously unsuspected regulatory dimensions, in that presence of functional proteins may no longer be inferred from mRNA, but rather depend on post-transcriptional regulation possibly modulated through metabolism

Nuclear Reprogramming: Kinetics of Cell Cycle and Metabolic Progression as Determinants of Success

Establishment of totipotency after somatic cell nuclear transfer (NT) requires not only reprogramming of gene expression, but also conversion of the cell cycle from quiescence to the precisely timed sequence of embryonic cleavage. Inadequate adaptation of the somatic nucleus to the embryonic cell cycle regime may lay the foundation for NT embryo failure and their reported lower cell counts. We combined bright field and fluorescence imaging of histone H2b-GFP expressing mouse embryos, to record cell divisions up to the blastocyst stage. This allowed us to quantitatively analyze cleavage kinetics of cloned embryos and revealed an extended and inconstant duration of the second and third cell cycles compared to fertilized controls generated by intracytoplasmic sperm injection (ICSI). Compared to fertilized embryos, slow and fast cleaving NT embryos presented similar rates of errors in M phase, but were considerably less tolerant to mitotic errors and underwent cleavage arrest. Although NT embryos vary substantially in their speed of cell cycle progression, transcriptome analysis did not detect systematic differences between fast and slow NT embryos. Profiling of amino acid turnover during pre-implantation development revealed that NT embryos consume lower amounts of amino acids, in particular arginine, than fertilized embryos until morula stage. An increased arginine supplementation enhanced development to blastocyst and increased embryo cell numbers. We conclude that a cell cycle delay, which is independent of pluripotency marker reactivation, and metabolic restraints reduce cell counts of NT embryos and impede their development

Generalization of an Outlier Model into a Global Perspective

n this work, we study the generalization of an outlier model from two perspectives, temporal and spatial. We show that model generalization with existing distribution-based outlier analysis methods can vary significantly. We then propose a “big data” outlier analysis approach together with a probability-based outlier evaluation for improving model generalization. Experiments are conducted based on two automotive product lines to explain the concepts and demonstrate the effectiveness of the proposed approach

Consistency in Wafer Based Outlier Screening

Outlier screening is a popular approach for testing automotive products. In practice, developing an outlier model can be subjective, making justification of the model challenging. In this paper we propose a new concept called Consistency which provides a data-driven objective way to assess an outlier model. We study the development of outlier models in view of this new model consistency concept and report experimental findings on an automotive product line

Time-lapse cinematography of ICSI and NT mouse embryos.

<p>A) Until 48 hours post activation (hpa), bright-field images were captured every 20 minutes. From 48 until 96 hpa, confocal optical sections of <i>H2b-GFP</i> expressing embryos were captured every 20 minutes. Time-lapse movies were evaluated to obtain the timing of cleavages. B) Cell division aberrancies such as failing cytokinesis (top panel; filled arrows) or cell fusions (bottom panel; empty arrows) were detected in both NT and ICSI embryos. Dotted lines indicate cell membranes. Scale bar, 20 µm.</p

Cleavage pace predicts blastocyst formation of cloned embryos.

<p>Embryos were scored <i>fast</i> or <i>slow</i> according to the time spent until the three-cell stage was reached. Then blastocyst formation was recorded, and ESCs were derived (data pooled from 4 experiments), or fetal rates were determined at E10.5 after transfer in utero 12 hours after scoring (data pooled from 2 experiments). The <i>p</i>-value of Fisher's exact test shows that the difference in blastocyst formation of <i>fast</i> and <i>slow</i> is significant, difference in ESC formation efficiency is of borderline significance, and difference in fetal formation is not significant. Data were pooled from five independent NT experiments. Development data from fertilized control embryos (ICSI) are shown in bottom row. Note that fertilized embryos were not sorted into fast and slow, and therefore frequencies relate to the two-cell stage. Derived ESCs were pluripotent regardless of their origin (fast, slow) as demonstrated by in vitro differentiation into derivatives of the three germ layers (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035322#pone.0035322.s013" target="_blank">figure S5</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035322#pone.0035322.s006" target="_blank">movies S6</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035322#pone.0035322.s007" target="_blank">S7</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035322#pone.0035322.s008" target="_blank">S8</a>) and by teratoma formation (data not shown).</p

Scatter plot of gene expression levels.

<p>A) NT <i>fast</i> and NT <i>slow</i> largely overlap (5 genes differently expressed, red/blue dots), B) ICSI <i>fast</i> and ICSI <i>slow</i> also largely overlap (34 genes differently expressed, red/blue dots), C) NT and ICSI show great differences in gene expression (218 genes differently expressed, red/blue dots). D) Fold-change of gene expression in ICSI <i>fast</i> versus ICSI <i>slow</i> and NT <i>fast</i> versus NT <i>slow</i>. Red dots, oocyte/1-cell-specific (<i>maternal</i>) transcripts; blue dots, 4-cell/blastocyst-specific transcripts (<i>embryonic</i>); black dots, transcripts not different between maternal and embryonic stages; grey dots, transcripts not represented in downloaded data sets. Embryonic transcripts are over-represented in <i>fast</i> ICSI embryos.</p

Cell cycle duration of ICSI and NT mouse embryos in hours, starting from activation.

<p>Green, cell cycle 1 (one-cell stage); blue, cell cycle 2 (two-cell stage); red, cell cycle 3 (four-cell stage); orange, cell cycle 4 (eight-cell stage). Boxed, inter-stages (three-cell stage, five- to seven-cell stage, nine- to fifteen-cell stage). Centred black numbers indicate median length of the entire cleavage stage; white numbers indicate the inter-stage length only. Error bars, median average deviation of entire cleavage stage length; <i>n</i>, number of embryos at respective stage. *, significantly different from same cell cycle in ICSI (Wilcoxon rank sum test, <i>p</i><0.01).</p

Proposed model of reprogramming mode, adapted from Hanna et al. [<b>8</b>].

<p>After NT, reprogramming proceeds with directed and stochastic components (A) with variable latency to yield embryos of different developmental stages (B). Due to the stochastic component of the reprogramming process, some embryos have been reprogrammed more than others at a certain point in time (C). If a critical reprogramming threshold of genes essential for the respective chronologically timed embryonic stage is not reached, the embryo halts development (C). It cannot be excluded that certain oocytes reprogram better than others (elite oocytes, D), for example due to higher levels of certain factors of the “reprogrammome” <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035322#pone.0035322-Pfeiffer1" target="_blank">[61]</a>.</p